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Using an Exact Radial Basis Function Artificial Neural Network for Impulsive Noise Suppression from Highly Distorted Image Databases

  • Pınar Çivicioğlu
  • Mustafa Alçı
  • Erkan Beṣdok
Part of the Lecture Notes in Computer Science book series (LNCS, volume 3261)

Abstract

In this paper, a new filter, RM, which is based on exact radial basis function artificial neural networks, is proposed for the impulsive noise suppression from highly distorted images. The RM uses Chi-Squared based goodness-of-fit test in order to find corrupted pixels more accurately.The proposed filter shows a high performance at the restoration of images distorted by impulsive noise. The extensive simulation results show that the proposed filter achieves a superior performance to the other filters mentioned in this paper in the cases of being effective in noise suppression and detail preservation, especially when the noise density is very high.

Keywords

Radial Basis Function Radial Basis Function Network Impulse Noise Impulsive Noise Noise Density 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer-Verlag Berlin Heidelberg 2004

Authors and Affiliations

  • Pınar Çivicioğlu
    • 1
  • Mustafa Alçı
    • 2
  • Erkan Beṣdok
    • 3
  1. 1.Civil Aviation School, Avionics Dept.Erciyes UniversityKayseriTurkey
  2. 2.Engineering Faculty, Electronic Engineering Dept.Erciyes UniversityKayseriTurkey
  3. 3.Engineering Faculty, Geodesy and Photogrammetry Engineering Dept.Erciyes UniversityKayseriTurkey

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